{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:33:40Z","timestamp":1760146420496,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62301534","GZB20230777","2024T170951","2024M753306"],"award-info":[{"award-number":["62301534","GZB20230777","2024T170951","2024M753306"]}]},{"name":"Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation","award":["62301534","GZB20230777","2024T170951","2024M753306"],"award-info":[{"award-number":["62301534","GZB20230777","2024T170951","2024M753306"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62301534","GZB20230777","2024T170951","2024M753306"],"award-info":[{"award-number":["62301534","GZB20230777","2024T170951","2024M753306"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62301534","GZB20230777","2024T170951","2024M753306"],"award-info":[{"award-number":["62301534","GZB20230777","2024T170951","2024M753306"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets.<\/jats:p>","DOI":"10.3390\/rs16224125","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T08:25:14Z","timestamp":1730795114000},"page":"4125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Qiong","family":"Ran","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Hong","family":"Zhong","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5389-7251","authenticated-orcid":false,"given":"Xu","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7524-1392","authenticated-orcid":false,"given":"Degang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"He","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging Spectrometry for Earth Remote Sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral Image Data Analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. 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